In multi-agent systems, goal achievement is challenging when agents operate in ever-
changing environments and face unseen situations, where not all the goals are known or …

Reinforcement Learning (RL) can enhance the adjustment of the traffic signals' phases to
improve the traffic flow. RL methods use ontologies and reasoning to enrich the controllers' …

Decision-making is challenging for agents operating in partially observable environments. In
such environments, agents' observation is often based on incomplete, ambiguous, and noisy …

Agents, whether software or hardware, perceive their environment through sensors and act
using actuators, often operating in dynamic, partially observable settings. They face …

This research presents a very efficient machine learning method based on Conditional
Random Fields (CRF) for the extraction of multi-document summaries. We have used the …

Several mining algorithms have been invented over the course of recent decades. However,
many of the invented algorithms are confined to generating frequent patterns and do not …

This thesis presents a framework of studies on theories, methodologies, algorithms, and
experiments on cognitive knowledge bases (CKBs) for machine knowledge learning in …